Github user cenyuhai commented on a diff in the pull request:

    https://github.com/apache/spark/pull/15041#discussion_r79754097
  
    --- Diff: core/src/main/scala/org/apache/spark/util/collection/Utils.scala 
---
    @@ -30,10 +34,22 @@ private[spark] object Utils {
        * Returns the first K elements from the input as defined by the 
specified implicit Ordering[T]
        * and maintains the ordering.
        */
    -  def takeOrdered[T](input: Iterator[T], num: Int)(implicit ord: 
Ordering[T]): Iterator[T] = {
    -    val ordering = new GuavaOrdering[T] {
    -      override def compare(l: T, r: T): Int = ord.compare(l, r)
    +  def takeOrdered[T](input: Iterator[T], num: Int,
    +      ser: Serializer = SparkEnv.get.serializer)(implicit ord: 
Ordering[T]): Iterator[T] = {
    +    val context = TaskContext.get()
    +    if (context == null) {
    +      val ordering = new GuavaOrdering[T] {
    +        override def compare(l: T, r: T): Int = ord.compare(l, r)
    +      }
    +      ordering.leastOf(input.asJava, num).iterator.asScala
    +    } else {
    +      val sorter =
    +        new ExternalSorter[T, Any, Any](context, None, None, Some(ord), 
ser)
    +      sorter.insertAll(input.map(x => (x, null)))
    --- End diff --
    
    1.In my case,  user execute a sql "select * from table sort by time limit 
10000000", the k is very large, it's an extreme case. I need not change 
RDD.takeOrdered. I will limit the changes in limit.scala.
    2. GuavaOrdering will sort all data in memory and then take top k. If there 
is enough memory, ExternalSorter will not spill.


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